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Account Research Coverage — Trailing Mean Series + Explicit Missing-Value Fill
x402
Account Research Coverage — Trailing Mean Series + Explicit Missing-Value Fill is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-15).
Computes a trailing moving-average time series and fills missing values for account research coverage data (researched_accounts and target_accounts fields) across up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Account research coverage — Trailing mean series + Explicit missing-value fill: Trailing mean series + Explicit missing-value fill for supplied account research coverage records (researched_accounts, target_accounts). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows.
Output
Returns two analytical reports for the submitted dataset: (1) a trailing moving-average series computed over the specified window for the chosen field, and (2) the same dataset with explicit missing-value imputation applied using the selected method (zero, mean, or median). Results cover up to 200 input rows with no external lookups or predictions.
Example use cases
Smoothing sales team account coverage trends
“I have daily account research coverage records for two company groups over the last quarter — can you compute a trailing mean with a window size of 7 for the researched_accounts field and fill any missing values with the mean so I can see a smoothed trend?”
Imputing gaps in target accounts pipeline data
“My target_accounts data has some null entries across 120 rows grouped by company — can you fill those missing values using zero-fill and also generate a trailing mean series with a window of 5 so I can spot coverage patterns?”
Comparing coverage metrics across company groups
“I've got about 80 rows of account research coverage records split across three company groups with dates — can you run the trailing mean series with a window of 4 and use median imputation on researched_accounts so I can compare smoothed coverage across groups?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-company-08-154?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have tabular account research or sales pipeline coverage data with a date dimension and need both a smoothed rolling average and missing-value imputation in a single call. It is ideal for small-to-medium datasets (up to 200 rows) grouped by company where you want local analytical computation without any external data lookups, predictions, or live integrations. Prefer this over general-purpose analytics services when you specifically need trailing mean smoothing combined with explicit null filling for account coverage metrics at low cost.
Side effects
- Input array is empty or exceeds 200 rows — rejected with validation error
- Missing required fields (row_id, date, company_group) in any row — schema validation failure
- date field does not match YYYY-MM-DD format — pattern validation error
- window value is less than 2 or greater than 200 — out-of-range error
- field enum value is not 'researched_accounts' or 'target_accounts' — invalid enum error
- method enum is not 'zero', 'mean', or 'median' — invalid enum error
- Payment of 0.01 USDC not received — 402 Payment Required response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string | |
| window | integer |
Tags
x402
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